A critical unsolved problem in neuroscience is to understand how learning is implemented by the nervous system. However, despite years of experimental study, our understanding of how patterns of electrical activity in the brain, and in the muscles that implement behavior, is still rudimentary. A critical gap therefore exists in our understanding of the biological and computational underpinnings of motor learning. Songbirds provide a physiologically accessible model system in which to investigate how motor memories are formed during development and executed in adulthood. The proposed studies exploit the strength of the songbird model to test a novel theory of motor learning, linking changes in the patterns of electrical activity in neurons and muscle fibers to the improvements in motor performance that typify skill learning. Our long-term goal is to fully understand how neural codes change during learning. The objective of the proposed experiments is to understand the adaptive control of a vocal acoustics in birdsong. Our central hypothesis is that that the fundamental computation underlying motor learning is the brain's search for the precisely-timed spike patterns that exploit muscle biomechanics to achieve the desired behavior. This hypothesis emerges from our previous work demonstrating that in adult songbirds, which have fully learned their songs, both neurons and vocal muscle fibers employ a spike-timing based code (rather than a rate-based code) to control vocal behavior. These results, combined with other data showing that the statistics of neural activity change dramatically as a bird first learns to sing, suggest that the key change underlying vocal skill learning is a developmental transition from a rate-based spike code to a timing-based code. Employing a number of novel experimental and computational tools that we have developed, we will test our hypothesis in three specific aims. In the first aim, we will compare the spiking code employed by neurons in the motor cortex before and after young birds learn to sing, providing insight into whether and how the brain's neural strategies for motor control change during learning. In the second aim, we will analyze spiking data from muscle fibers over the same interval, allowing us to determine how muscle codes change with learning, and, using innovative in vitro and ex vivo approaches, quantify how changes in muscle activation improve motor performance in the face of developmental changes in biomechanics. In the third aim, we will record spiking activity from individual motor units continuously while adult birds perform a rapid motor learning task, revealing the changes that occur within a single unit during learning.